Day-ahead price forecasting of electricity markets by a new fuzzy neural network

By Nima Amjady. 2006

388 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, methodology): “(2005), the fuzzy neural network of Amjady (2006), and the neural network of Catalão, Mariano, Mendes, and Ferreira (2007), and also over the ‘mixed model’ of Garcia-Martos, Rodriguez, and Sanchez (2007) in three test periods out of four.”
A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method (2007, methodology): “For example, time-series [7], ARIMA model [11], GARCH model [2], artiﬁcial neural networks (ANN) [5], [10], [12]–[14], wavelet transform and ARIMA models [23], agents-based simulations [16], input-output hidden Markov models (IOHMM) [15], fuzzy neural network [24], etc.”
An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets (2008, methodology): “For both the markets, hourly historical price data of the 48 days previous to the day of the week whose prices are to be forecasted have been considered to build the forecasting model [10], [13], and [17].”
Machine learning in energy economics and finance: A review (2019, methodology): “ANN models were widely used to forecast electricity prices in the early 2000s (Amjady (2006), see Aggarwal et al. (2009) for a review of early papers).”
Electricity price forecasting in deregulated markets: A review and evaluation (2009, methodology): “04% [50] PP 48 4 weeks of 4 seasons 1 DA Average weekly MAPE = 7.”
Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services (2012, methodology): “Day-ahead load forecasts are the actual load forecasts used in the ERCOT system [25], and the day-ahead price forecasts are generated to statistically match the error distributions found in [27].”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, background): “The list is headed by Nima Amjady (Semnan University, Iran), who has (co-)authored 16 EPF articles, including the influential Day-ahead price forecasting of electricity markets by a new fuzzy neural network [26] (175 citations in Scopus since 2006; w/o self citations).”
Density Forecasting for Long-Term Peak Electricity Demand (2010, methodology): “(Kermanshahi, 1998; Hippert et al., 2001; Amjady, 2006, 2007; Kandil et al., 2002; Song”
Optimal Charging Strategies for Unidirectional Vehicle-to-Grid (2011, background): “Day-ahead price forecasts are generated to statistically match those error distributions found in [22].”